update llama3 example base models to use nous

examples/llama-3/fft-8b.yaml

@@ -1,4 +1,4 @@
-base_model: meta-llama/Meta-Llama-3-8B
+base_model: NousResearch/Meta-Llama-3-8B
 model_type: LlamaForCausalLM
 tokenizer_type: AutoTokenizer
 

examples/llama-3/lora-8b.yml

@@ -1,4 +1,4 @@
-base_model: meta-llama/Meta-Llama-3-8B
+base_model: NousResearch/Meta-Llama-3-8B
 model_type: LlamaForCausalLM
 tokenizer_type: AutoTokenizer
 

examples/llama-3/qlora-fsdp-70b.yaml

@@ -1,4 +1,4 @@
-base_model: casperhansen/llama-3-70b-fp16
+base_model: NousResearch/Meta-Llama-3-70B
 model_type: LlamaForCausalLM
 tokenizer_type: AutoTokenizer  # PreTrainedTokenizerFast
 

examples/llama-3/qlora.yml

@@ -1,4 +1,4 @@
-base_model: meta-llama/Meta-Llama-3-8B
+base_model: NousResearch/Meta-Llama-3-8B
 model_type: AutoModelForCausalLM
 tokenizer_type: AutoTokenizer
 
@@ -7,7 +7,7 @@ load_in_4bit: true
 strict: false
 
 datasets:
-  - path: aaditya/alpaca_subset_1
+  - path: tatsu-lab/alpaca
     type: alpaca
 dataset_prepared_path:
 val_set_size: 0

setup.py

@@ -108,7 +108,6 @@ setup(
             "galore_torch",
             "lion-pytorch==0.1.2",
             "lomo-optim==0.1.1",
-            "q-galore-torch==1.0",
             "torch-optimi==0.2.1",
         ],
     },

src/axolotl/core/trainer_builder.py

@@ -293,8 +293,7 @@ class AxolotlTrainer(Trainer):
     def create_optimizer(self):
         if (
             self.args.loraplus_lr_ratio is None
-            and self.args.alternate_optimizer
-            not in ["optimi_adamw", "q_galore_adamw8bit"]
+            and self.args.alternate_optimizer != "optimi_adamw"
         ):
             return super().create_optimizer()
 
@@ -345,12 +344,6 @@ class AxolotlTrainer(Trainer):
                         optimizer_grouped_parameters, foreach=False, **optimizer_kwargs
                     )
                 )
-            elif self.args.alternate_optimizer == "q_galore_adamw8bit":
-                from q_galore_torch import QGaLoreAdamW8bit
-
-                self.optimizer = (  # pylint: disable=attribute-defined-outside-init
-                    QGaLoreAdamW8bit(optimizer_grouped_parameters, **optimizer_kwargs)
-                )
 
         if is_sagemaker_mp_enabled():
             self.optimizer = smp.DistributedOptimizer(  # pylint: disable=attribute-defined-outside-init
@@ -1443,7 +1436,7 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
 
         trainer_kwargs = {}
 
-        if self.cfg.optimizer in ["optimi_adamw", "q_galore_adamw8bit"]:
+        if self.cfg.optimizer == "optimi_adamw":
             # Set default so transformers doesn't throw
             training_arguments_kwargs["optim"] = "adamw_hf"
             training_arguments_kwargs["alternate_optimizer"] = self.cfg.optimizer

src/axolotl/utils/config/models/input/v0_4_1/__init__.py

@@ -341,10 +341,7 @@ class HyperparametersConfig(BaseModel):
     learning_rate: Union[str, float]
     weight_decay: Optional[float] = 0.0
     optimizer: Optional[
-        Union[
-            OptimizerNames,
-            Literal["lion_pytorch", "optimi_adamw", "q_galore_adamw8bit"],
-        ]
+        Union[OptimizerNames, Literal["lion_pytorch", "optimi_adamw"]]
     ] = OptimizerNames.ADAMW_HF.value
     optim_args: Optional[Union[str, Dict[str, Any]]] = Field(
         default=None, metadata={"help": "Optional arguments to supply to optimizer."}

tests/e2e/test_optimizers.py

@@ -65,45 +65,3 @@ class TestCustomOptimizers(unittest.TestCase):
 
         train(cfg=cfg, cli_args=cli_args, dataset_meta=dataset_meta)
         assert (Path(temp_dir) / "adapter_model.bin").exists()
-
-    @with_temp_dir
-    def test_q_galore_adamw8bit(self, temp_dir):
-        # pylint: disable=duplicate-code
-        cfg = DictDefault(
-            {
-                "base_model": "JackFram/llama-68m",
-                "tokenizer_type": "LlamaTokenizer",
-                "sequence_len": 1024,
-                "load_in_8bit": True,
-                "adapter": "lora",
-                "lora_r": 8,
-                "lora_alpha": 16,
-                "lora_dropout": 0.05,
-                "lora_target_linear": True,
-                "val_set_size": 0.1,
-                "special_tokens": {
-                    "unk_token": "<unk>",
-                    "bos_token": "<s>",
-                    "eos_token": "</s>",
-                },
-                "datasets": [
-                    {
-                        "path": "mhenrichsen/alpaca_2k_test",
-                        "type": "alpaca",
-                    },
-                ],
-                "num_epochs": 1,
-                "micro_batch_size": 8,
-                "gradient_accumulation_steps": 1,
-                "output_dir": temp_dir,
-                "learning_rate": 0.00001,
-                "optimizer": "q_galore_adamw8bit",
-                "lr_scheduler": "cosine",
-            }
-        )
-        normalize_config(cfg)
-        cli_args = TrainerCliArgs()
-        dataset_meta = load_datasets(cfg=cfg, cli_args=cli_args)
-
-        train(cfg=cfg, cli_args=cli_args, dataset_meta=dataset_meta)
-        assert (Path(temp_dir) / "adapter_model.bin").exists()